ArcCollector: UW-Eau Claire Campus Microclimate

INTRODUCTION

Smart phones and tablets have become extremely useful in the field of GIS because they have stronger computing power than most GPS units and can easily access online data for instant-upload capability all with a smooth, familiar user interface. This project involves a collective data gathering method among multiple users to produce micro-climate information for the UW-Eau Claire campus.

METHODS

The University of Wisconsin-Eau Claire's campus was split into seven zones (Figure 1). Each student was then assigned a zone, randomly sampling points throughout using ArcCollector. 
Figure 1: Micro-climate point data and zones collected in a project in ArcCollector
ArcCollector was downloaded to all students' phones and a group was created to access a common project and view the interactive map while collecting data points. Attributes collected were temperature, dew point, wind speed, wind chill, wind direction, and time. Using the group feature on ArcGIS Online, the data was then compiled into one dataset. The dataset was then saved to ArcMap and made into a feature class within a file geodatabase to create a series of maps representing the campus micro-climate. 

RESULTS

Figure 2 shows the micro-climate of the UW-Eau Claire campus by wind patterns. This map included the attributes of wind speed, direction, and temperature to give a comprehensive visualization of how campus might feel on a typical day in late October.
Figure 2: Campus wind patterns gathered in ArcCollector
Figure 3 shows the temperature of campus using the IDW interpolation function to give a relative pattern of temperature variance depending on the location on campus.
Figure 3: Campus temperature map gathered in ArcCollector

Figure 4 shows the dew point on campus to visualize the relative difference in dryness and humidity depending on the location on campus.
Figure 4: Campus dew point map gathered in ArcCollector

CONCLUSIONS

ArcCollector is a great method of collecting data in the field and collectively working on a project with real-time updates. Establishing proper attributes and noting important features during collection is good practice. A number of points needed to be deleted after collection because for whatever reason, the attribute value was notably higher or lower due to a manual error. It's important to identify these points before processing, allowing proper symbolization to occur. 

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